Molecular biology and all the biomedical sciences are undergoing a true revolution as a result of the emergence and growing impact of a series of new disciplines/tools sharing the "-omics" suffix in their name. These include in particular genomics, transcriptomics, proteomics and metabolomics, devoted respectively to the examination of the entire systems of genes, transcripts, proteins and metabolites present in a given cell or tissue type.
The availability of these new, highly effective tools for biological exploration is dramatically changing the way one performs research in at least two respects. First, the amount of available experimental data is not a limiting factor any more; on the contrary, there is a plethora of it. Given the research question, the challenge has shifted towards identifying the relevant pieces of information and making sense out of it (a "data mining" issue). Second, rather than focus on components in isolation, we can now try to understand how biological systems behave as a result of the integration and interaction between the individual components that one can now monitor simultaneously (so called "systems biology").
Taking advantage of this wealth of "genomic" information has become a conditio sine qua non for whoever ambitions to remain competitive in molecular biology and in the biomedical sciences in general. Machine learning naturally appears as one of the main drivers of progress in this context, where most of the targets of interest deal with complex structured objects: sequences, 2D and 3D structures or interaction networks. At the same time bioinformatics and systems biology have already induced significant new developments of general interest in machine learning, for example in the context of learning with structured data, graph inference, semi-supervised learning, system identification, and novel combinations of optimization and learning algorithms.
Topics
We
encourage submissions bringing forward methods for discovering complex
structures (e.g. interaction networks, molecule structures) and methods
supporting genome-wide data analysis. A non-exhaustive list of topics
suitable for this workshop are:
| Methods |
Applications |
| Machine Learning Algorithms |
Sequence Annotation |
| Bayesian Methods |
Gene Expression and post-transcriptional regulation |
| Data integration/fusion |
Inference of gene regulation networks |
| Feature/subspace selection |
Gene prediction and whole genome association studies |
| Clustering |
Metabolic pathway modeling |
| Biclustering/association rules |
Signaling networks |
| Kernel Methods |
Systems biology approaches to biomarker identification |
| Probabilistic inference |
Rational drug design methods |
| Structured output prediction |
Metabolic reconstruction |
| Systems identification |
Protein function and structure prediction |
| Graph inference, completion, smoothing |
Protein-protein interaction networks |
| Semi-supervised learning |
Synthetic biology |
Contact
